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      Call for Papers: Digital Diagnostic Techniques

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      About Pathobiology5.0 Impact Factor I 7.7 CiteScore I 1.088 Scimago Journal & Country Rank (SJR)

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      Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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          Abstract

          Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual’s disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson’s disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.

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          Most cited references15

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          Quantitative wearable sensors for objective assessment of Parkinson's disease.

          There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology may allow for continuous, unobtrusive, objective, and ecologically valid data collection. Also, this approach may improve patient-doctor interaction, influence therapeutic decisions, and ultimately ameliorate patients' global health status. In addition, such measures have the potential to be used as outcome parameters in clinical trials, allowing for frequent assessments; eg, in the home setting. This review discusses promising wearable technology, addresses which parameters should be prioritized in such assessment strategies, and reports about studies that have already investigated daily life issues in PD using this new technology. © 2013 Movement Disorder Society.
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            Rivastigmine for gait stability in patients with Parkinson's disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial.

            Falls are a frequent and serious complication of Parkinson's disease and are related partly to an underlying cholinergic deficit that contributes to gait and cognitive dysfunction in these patients. Gait dysfunction can lead to an increased variability of gait from one step to another, raising the likelihood of falls. In the ReSPonD trial we aimed to assess whether ameliorating this cholinergic deficit with the acetylcholinesterase inhibitor rivastigmine would reduce gait variability.
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              Wearable Devices in Clinical Trials: Hype and Hypothesis

              The development of innovative wearable technologies has raised great interest in new means of data collection in healthcare and biopharmaceutical research and development. Multiple applications for wearables have been identified in a number of therapeutic areas; however, researchers face many challenges in the clinic, including scientific methodology as well as regulatory, legal, and operational hurdles. To facilitate further evaluation and adoption of these technologies, we highlight methodological and logistical considerations for implementation in clinical trials, including key elements of analytical and clinical validation in the specific context of use (COU). Additionally, we provide an assessment of the maturity of the field and successful examples of recent clinical experiments.
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                Author and article information

                Journal
                DIB
                DIB
                10.1159/issn.2504-110X
                Digital Biomarkers
                S. Karger AG
                2504-110X
                2019
                September – December 2019
                07 October 2019
                : 3
                : 3
                : 116-132
                Affiliations
                [_a] aSchool of Computer Science, University of Birmingham, Birmingham, United Kingdom
                [_b] bDigital Medicine and Pfizer Innovation Research Lab, Early Clinical Development, Pfizer, Inc., Cambridge, Massachusetts, USA
                [_c] cCollege of Computer and Information Science, Northeastern University, Boston, Massachusetts, USA
                [_d] dGlobal Real World Data, Strategy, Analytics & Informatics (GRWD-SAI), Analytics, Informatics & Business Intelligence, Chief Digital Office, Pfizer, Inc., New York, New York, USA
                [_e] eThe Michael J. Fox Foundation for Parkinson’s Research, New York, New York, USA
                [_f] fMedia Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
                [_g] gUCB Biopharma, Brussels, Belgium
                [_h] hMachine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, Maryland, USA
                [_i] iBiogen, Cambridge, Massachusetts, USA
                [_j] jCritical Path Institute, Tucson, Arizona, USA
                [_k] kClinical Data Interchange Standards Consortium, Austin, Texas, USA
                [_l] lDepartment of Neurology, Christian Albrecht University, Kiel, Germany
                [_m] mJames J. and Joan A. Gardner Family Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA
                [_n] nDepartment of Neurology, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
                [_o] oDepartment of Neurology, Gardner Center for Parkinson’s Disease and Movement Disorders, UC Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, Ohio, USA
                [_p] pTufts University School of Medicine, Boston, Massachusetts, USA
                [_q] qHealthMode, New York, New York, USA
                Author notes
                *Reham Badawy, School of Engineering and Applied Science, Aston University, Birmingham B4 7ET (UK), E-Mail rehambadawy@hotmail.com
                Article
                502951 Digit Biomark 2019;3:116–132
                10.1159/000502951
                32175520
                bc7a9c89-ac99-4380-bd95-10412e1dfaba
                © 2019 The Author(s) Published by S. Karger AG, Basel

                This article is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND). Usage and distribution for commercial purposes as well as any distribution of modified material requires written permission. Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

                History
                : 25 March 2019
                : 26 August 2019
                Page count
                Figures: 5, Tables: 4, Pages: 17
                Categories
                Research Reports - Research Article

                Oncology & Radiotherapy,Geriatric medicine,Cardiovascular Medicine,Clinical Psychology & Psychiatry,Public health
                Parkinson’s disease,Digital health technology,Sensors,Objective monitoring of motor symptoms,Metadata

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